A critical investigation of recall and precision as measures of retrieval system performance
ACM Transactions on Information Systems (TOIS)
Foundations of statistical natural language processing
Foundations of statistical natural language processing
ROC confidence bands: an empirical evaluation
ICML '05 Proceedings of the 22nd international conference on Machine learning
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
3D model comparison using spatial structure circular descriptor
Pattern Recognition
Design and Analysis of Classifier Learning Experiments in Bioinformatics: Survey and Case Studies
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The Precision-Recall (PR) curve is a widely used visual tool to evaluate the performance of scoring functions in regards to their capacities to discriminate between two populations. The purpose of this paper is to examine both theoretical and practical issues related to the statistical estimation of PR curves based on classification data. Consistency and asymptotic normality of the empirical counterpart of the PR curve in sup norm are rigorously established. Eventually, the issue of building confidence bands in the PR space is considered and a specific resampling procedure based on a smoothed and truncated version of the empirical distribution of the data is promoted. Arguments of theoretical and computational nature are presented to explain why such a bootstrap is preferable to a "naive" bootstrap in this setup.